Its related ai

PAWS Digital Interactive Platform for industries can simulate huge and complex projects by leveraging advanced technologies like 3D visualization and AI in Metaverse. We designed 3 levels of using the industrial metaverse. marketing level that shows only the general aspects of the industrial plant. knowledge management that helps e-learning, reducing HSE risks, and real-time data gathering…

The fixed-wing VTOL UAV is a versatile drone designed for wildlife monitoring, vegetation analysis, and crisis disaster detection missions. Combining vertical takeoff and landing with efficient long-range flight, it can cover large areas quickly and access hard-to-reach locations. Equipped with advanced sensors like thermal imaging, multispectral, and high-resolution cameras, it provides real-time data for tracking wildlife, areas with vegetation cover, and identifying disaster zones. Its autonomous mission capabilities allow for extended monitoring and rapid response in critical situations, making it ideal for environmental and emergency operations.

The ViraStrata drone is equipped with magnetometer and hyperspectral sensors, designed to detect and map mineral deposits. The magnetometer identifies variations in the Earth’s magnetic field caused by underground metal-rich formations, while the hyperspectral sensors analyze surface composition by capturing a wide spectrum of light. Together, these tools enable precise, non-invasive exploration for resource-rich areas in hard-to-reach or hazardous locations.
Accompanying the drone is a powerful software platform that analyzes the collected data. This software processes and interprets magnetometer readings and hyperspectral imagery, generating detailed maps and mineral composition models. It uses advanced algorithms to highlight potential resource hotspots, offering mining companies actionable insights and improving exploration efficiency.

Using drones and artificial intelligence to troubleshoot solar power plants is done in several ways:

1. Thermal imaging: UAVs take infrared images of solar panels using thermal cameras. These images help to identify hot spots that indicate problems such as bypass diodes or open circuit threads.

2. Analysis of images with artificial intelligence: Artificial intelligence analyzes the images taken by drones to automatically identify anomalies. This method helps to reduce inspection time and costs and is more accurate than manual inspections.

3. Geographical tagging: The images taken by drones have geographic tags that help to accurately identify the location of defects. This feature allows maintenance teams to access problem areas quickly and accurately.

4. Reduction of inspection time: Inspection of a large solar power plant using drones can be done in a few hours, while manual inspection may take several days

We have created a new and unique user-friendly web-based software called “AFTL” using deep learning techniques to detect over 80 types of defects in power transmission lines, including electrical, mechanical, and foundation issues such as damaged or broken insulators, shortage of bolts and nuts, corrosion, and rust. This is a national project with a Technology Readiness Level (TRL) of 8 and was chosen as the top project of Tavanir in 2020, 2022, and 2023. Tavanir is the main power generation, transmission, and distribution company in Iran. High-resolution RGB images were captured by UAVs (Drones) from power transmission lines for this project. The training and test datasets for the software were created by annotating more than six million images from faulty captures over a span of seven years using a specialized software called “Vira Label.” This software helps experts create labels around faults in UAV images and check them quickly. We also developed an end-to-end and user-friendly software, where users only need to upload the UAV images, and the report of faults can be easily downloaded in different formats such as PDF, Excel, and GIS. Technicians can use the results in the report to perform other analyses, such as estimating the expected average lifetime of components, and it also shows the exact geographic locations of faults in GIS format.

VATCD automatically detects tension cracks in an open-pit mine located in “Gohar Zamin-Sirjan, Kerman, Iran” based on deep learning algorithms. The size, locations, and developments of these tension cracks are typically used to predict slope failures and ensure safe mining operations.

Recently, remote sensing (RS) imagery acquired by UAVs has been investigated for counting objects such as plants, because high temporal and spatial resolution data can be acquired over large fields. Manual counting in the large orthophotos is similar to field-based manual counting in the sense that it is a subjective, tedious task and should be performed by experts because of the highly overlapped and complex shape of plants as they develop. Therefore, automatic and accurate techniques based on deep learning have gained particular attention.
We have developed significant algorithms based on few-shot learning for plant counting and flowering date estimation.

Railway networks across the globe play a crucial role in the transportation infrastructure and embody substantial investments. Neglected networks can have dire consequences for asset longevity, schedule performance, and overall safety. To mitigate these risks, Railroads conduct thorough inspections of their mainline network annually and key locations even more frequently. However, traditional manual inspection methods are not only costly and time-consuming but also pose risks to staff and impact schedule performance. Although attempts have been made to modernize the inspection process using machine-vision technologies, these still rely on human inspectors to manually review images, resulting in subjective and labor-intensive practices. This project introduces a groundbreaking approach utilizing Deep Learning algorithms, specifically a Deep Neural Network, to automatically inspect images, offering a solution that overcomes these limitations and revolutionizes railway inspection processes.

Automatic cartography Vira Kavir (ACVS) is an automatic software that has revolutionized the way maps are created. With ACVS, users can simply upload an orthophoto image, and the software will automatically generate a map based on the features and details present in the image. However, ACVS is not just limited to creating maps. It can also to detect the position of pipelines by detecting their trace on the ground or pavement.
ACVS works based on advanced image processing and deep learning techniques such as segmentation and object detection. Segmentation is used for determining roofs, land, and superstructure of buildings and roads. The segmentation method is based on transformers and can handle different sizes of objects. This allows the software to accurately identify and label different features in the image.
Object detection is used for detecting the position of pipelines. The object detection method comprises a super-resolution model and attention modules, which enable it to detect even the smallest details in the image. This algorithm also utilizes oriented bounding boxes for handling pipelines in different places on the pavement surface. This makes it an ideal tool for pipeline detection and mapping.
One of the key benefits of ACVS is its ability to create maps and detect pipelines quickly and efficiently. This is particularly useful in emergencies where time is of the essence, such as during natural disasters or pipeline leaks. The software can also be used for urban planning, environmental monitoring, and other applications.